Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:23:24.784023
Analysis finished2020-08-25 00:23:46.171366
Duration21.39 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3445969671010972e-11
Minimum-1.7880475521087646
Maximum1.719038009643555
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:46.218381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.788047552
5-th percentile-1.589020443
Q1-0.8198409528
median0.002392768831
Q30.8417787105
95-th percentile1.549982661
Maximum1.71903801
Range3.507085562
Interquartile range (IQR)1.661619663

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-7.43717282e+10
Kurtosis-1.145116223
Mean-1.344596967e-11
Median Absolute Deviation (MAD)0.8369789124
Skewness-0.001428697913
Sum-1.344596967e-08
Variance1.000000003
2020-08-25T00:23:46.323971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.409178733810.1%
 
0.0536297485210.1%
 
-0.193040415610.1%
 
-0.879582583910.1%
 
0.717468380910.1%
 
-0.234542474210.1%
 
0.665741443610.1%
 
0.580746054610.1%
 
0.930354952810.1%
 
0.191728502510.1%
 
0.398769229710.1%
 
-0.113607987810.1%
 
-0.0920735225110.1%
 
-0.400257557610.1%
 
1.09115922510.1%
 
1.39583647310.1%
 
1.50905585310.1%
 
-0.358724057710.1%
 
-0.756508886810.1%
 
-0.373124748510.1%
 
-0.938148379310.1%
 
0.129556149210.1%
 
-0.222632899910.1%
 
-1.60674929610.1%
 
0.427077174210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.78804755210.1%
 
-1.78470790410.1%
 
-1.77937412310.1%
 
-1.77568602610.1%
 
-1.76847076410.1%
 
-1.76075851910.1%
 
-1.75117874110.1%
 
-1.74841594710.1%
 
-1.74133360410.1%
 
-1.73685693710.1%
 
ValueCountFrequency (%) 
1.7190380110.1%
 
1.71277546910.1%
 
1.71084964310.1%
 
1.7063965810.1%
 
1.70092475410.1%
 
1.69730639510.1%
 
1.69198489210.1%
 
1.68838357910.1%
 
1.68236684810.1%
 
1.67928469210.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3669563233852387e-09
Minimum-1.7812438011169434
Maximum1.8213061094284055
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:46.437317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.781243801
5-th percentile-1.561071551
Q1-0.8229633868
median0.01028001774
Q30.8211746365
95-th percentile1.590781075
Maximum1.821306109
Range3.602549911
Interquartile range (IQR)1.644138023

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)422483503.6
Kurtosis-1.119458321
Mean2.366956323e-09
Median Absolute Deviation (MAD)0.824657321
Skewness0.03029220194
Sum2.366956323e-06
Variance1.000000001
2020-08-25T00:23:46.547508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.859374821210.1%
 
-1.35555994510.1%
 
-0.766293525710.1%
 
-1.22913241410.1%
 
0.144627645610.1%
 
-0.563163995710.1%
 
-1.43556201510.1%
 
-0.19157208510.1%
 
-1.70444738910.1%
 
1.51303863510.1%
 
1.28647565810.1%
 
1.28256356710.1%
 
-0.488609015910.1%
 
0.0426707230510.1%
 
0.34598007810.1%
 
-1.06770694310.1%
 
-0.410469919410.1%
 
-0.0206501539810.1%
 
1.52472901310.1%
 
0.582675397410.1%
 
-0.389969468110.1%
 
0.627593994110.1%
 
-1.51817381410.1%
 
1.41924357410.1%
 
-0.861963570110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.78124380110.1%
 
-1.7808097610.1%
 
-1.75868153610.1%
 
-1.75007295610.1%
 
-1.74951744110.1%
 
-1.74374771110.1%
 
-1.74357092410.1%
 
-1.74052083510.1%
 
-1.73753559610.1%
 
-1.73617911310.1%
 
ValueCountFrequency (%) 
1.82130610910.1%
 
1.81989657910.1%
 
1.81848359110.1%
 
1.8100199710.1%
 
1.808016310.1%
 
1.80668711710.1%
 
1.80054485810.1%
 
1.7962553510.1%
 
1.77878725510.1%
 
1.7681442510.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.062909960746765e-10
Minimum-1.7806508541107178
Maximum1.701529622077942
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:46.669312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.780650854
5-th percentile-1.587327141
Q1-0.8272039592
median0.01684245653
Q30.8964491487
95-th percentile1.520362264
Maximum1.701529622
Range3.482180476
Interquartile range (IQR)1.723653108

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1649373002
Kurtosis-1.174830016
Mean6.062909961e-10
Median Absolute Deviation (MAD)0.8625386674
Skewness-0.08955991833
Sum6.062909961e-07
Variance1.000000001
2020-08-25T00:23:46.774050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.748046338610.1%
 
1.40755903710.1%
 
0.106530800510.1%
 
0.93030548110.1%
 
-1.10292184410.1%
 
-0.379244387110.1%
 
-1.38416361810.1%
 
-0.860047340410.1%
 
-0.678404390810.1%
 
-1.76819491410.1%
 
-1.67438888510.1%
 
-1.39976429910.1%
 
-0.433924883610.1%
 
-1.20444703110.1%
 
0.157451748810.1%
 
-0.169108584510.1%
 
1.29422235510.1%
 
-1.09114611110.1%
 
-0.386067718310.1%
 
0.780057787910.1%
 
1.2334297910.1%
 
-1.10439324410.1%
 
1.13790845910.1%
 
0.736971199510.1%
 
-1.57159173510.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.78065085410.1%
 
-1.77991211410.1%
 
-1.76819491410.1%
 
-1.75863826310.1%
 
-1.75673580210.1%
 
-1.75461661810.1%
 
-1.75235104610.1%
 
-1.75118005310.1%
 
-1.73779559110.1%
 
-1.73360955710.1%
 
ValueCountFrequency (%) 
1.70152962210.1%
 
1.68295347710.1%
 
1.67859923810.1%
 
1.67367970910.1%
 
1.67213666410.1%
 
1.66825377910.1%
 
1.6678043610.1%
 
1.66522002210.1%
 
1.66348755410.1%
 
1.66220688810.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.622875273227692e-10
Minimum-1.778514385223389
Maximum1.6695576906204224
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:47.056693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.778514385
5-th percentile-1.620918757
Q1-0.8319297433
median0.08013848588
Q30.8467981964
95-th percentile1.516714954
Maximum1.669557691
Range3.448072076
Interquartile range (IQR)1.67872794

Descriptive statistics

Standard deviation0.9999999976
Coefficient of variation (CV)-1311840955
Kurtosis-1.170898926
Mean-7.622875273e-10
Median Absolute Deviation (MAD)0.836063683
Skewness-0.1371773074
Sum-7.622875273e-07
Variance0.9999999952
2020-08-25T00:23:47.162579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.08203077310.1%
 
1.55206680310.1%
 
1.3060262210.1%
 
-0.0708842650110.1%
 
0.321622818710.1%
 
-0.824885010710.1%
 
1.36070227610.1%
 
0.754567444310.1%
 
-1.10678911210.1%
 
0.447595059910.1%
 
0.0141062298810.1%
 
0.902998566610.1%
 
-0.195440858610.1%
 
-0.887367069710.1%
 
-0.102699637410.1%
 
0.0420634895610.1%
 
0.0536268018210.1%
 
1.44268512710.1%
 
-1.67841124510.1%
 
0.0804014876510.1%
 
-0.731255650510.1%
 
1.00126159210.1%
 
1.09891045110.1%
 
0.460624933210.1%
 
-1.39968478710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.77851438510.1%
 
-1.77335548410.1%
 
-1.773012410.1%
 
-1.77042627310.1%
 
-1.76803433910.1%
 
-1.76190638510.1%
 
-1.75640714210.1%
 
-1.75549805210.1%
 
-1.75488817710.1%
 
-1.75458109410.1%
 
ValueCountFrequency (%) 
1.66955769110.1%
 
1.66735947110.1%
 
1.65988898310.1%
 
1.65794348710.1%
 
1.65320622910.1%
 
1.65020275110.1%
 
1.6483510.1%
 
1.63191926510.1%
 
1.62674844310.1%
 
1.62480616610.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.792849257588386e-10
Minimum-1.7626078128814695
Maximum1.6636035442352295
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:47.276167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.762607813
5-th percentile-1.574196851
Q1-0.8821546733
median0.01230113581
Q30.8816696554
95-th percentile1.523687953
Maximum1.663603544
Range3.426211357
Interquartile range (IQR)1.763824329

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)1137287781
Kurtosis-1.243365437
Mean8.792849258e-10
Median Absolute Deviation (MAD)0.8868643939
Skewness-0.03491936703
Sum8.792849258e-07
Variance1.000000004
2020-08-25T00:23:47.377839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.41138660910.1%
 
1.50679540610.1%
 
1.51308703410.1%
 
0.959663808310.1%
 
-0.514106333310.1%
 
-0.609278857710.1%
 
-0.250337868910.1%
 
1.27990305410.1%
 
0.284515410710.1%
 
0.351897090710.1%
 
1.38147711810.1%
 
0.567073047210.1%
 
0.857575178110.1%
 
0.787769377210.1%
 
-0.46549081810.1%
 
1.4713431610.1%
 
0.514302849810.1%
 
-1.18490445610.1%
 
0.811200916810.1%
 
-1.67199289810.1%
 
1.33368825910.1%
 
-1.73176777410.1%
 
-1.04817044710.1%
 
-0.472978383310.1%
 
-1.27472257610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.76260781310.1%
 
-1.7495747810.1%
 
-1.74825084210.1%
 
-1.73385226710.1%
 
-1.73268544710.1%
 
-1.73194789910.1%
 
-1.73176777410.1%
 
-1.72703611910.1%
 
-1.71965694410.1%
 
-1.71831810510.1%
 
ValueCountFrequency (%) 
1.66360354410.1%
 
1.66284024710.1%
 
1.65921127810.1%
 
1.6584496510.1%
 
1.65795481210.1%
 
1.6550906910.1%
 
1.65004897110.1%
 
1.64786875210.1%
 
1.64707946810.1%
 
1.6422156110.1%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.403984010219574e-10
Minimum-1.7531616687774658
Maximum1.6611177921295166
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:47.491745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.753161669
5-th percentile-1.601113904
Q1-0.8542067111
median0.01373978984
Q30.8944012821
95-th percentile1.529747266
Maximum1.661117792
Range3.414279461
Interquartile range (IQR)1.748607993

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-2937734132
Kurtosis-1.217773609
Mean-3.40398401e-10
Median Absolute Deviation (MAD)0.8747264193
Skewness-0.04799752633
Sum-3.40398401e-07
Variance1.000000002
2020-08-25T00:23:47.598536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.58203065410.1%
 
0.15006810.1%
 
-0.701857566810.1%
 
0.28337529310.1%
 
-0.0516443178110.1%
 
0.676461696610.1%
 
1.08067166810.1%
 
0.36947998410.1%
 
-0.234056025710.1%
 
0.0116471992810.1%
 
1.39884841410.1%
 
0.400725722310.1%
 
-0.580746948710.1%
 
1.51429355110.1%
 
1.51695513710.1%
 
1.09015035610.1%
 
-0.32554861910.1%
 
0.610033929310.1%
 
-0.938800752210.1%
 
-0.949869394310.1%
 
1.56119954610.1%
 
1.6575435410.1%
 
-0.705723941310.1%
 
-0.315751999610.1%
 
-1.5794110310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75316166910.1%
 
-1.75002121910.1%
 
-1.74915623710.1%
 
-1.74875998510.1%
 
-1.74594414210.1%
 
-1.74542188610.1%
 
-1.73927664810.1%
 
-1.73840260510.1%
 
-1.73519730610.1%
 
-1.73363733310.1%
 
ValueCountFrequency (%) 
1.66111779210.1%
 
1.6590316310.1%
 
1.65773642110.1%
 
1.6575435410.1%
 
1.65351295510.1%
 
1.6526197210.1%
 
1.64644396310.1%
 
1.64471149410.1%
 
1.63946831210.1%
 
1.63418674510.1%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7585407476872206e-09
Minimum-1.7296241521835327
Maximum1.749320387840271
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:47.721301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.729624152
5-th percentile-1.551447141
Q1-0.8680442721
median-0.001800717087
Q30.8510795981
95-th percentile1.577883005
Maximum1.749320388
Range3.47894454
Interquartile range (IQR)1.71912387

Descriptive statistics

Standard deviation0.9999999983
Coefficient of variation (CV)-568653299.4
Kurtosis-1.180594665
Mean-1.758540748e-09
Median Absolute Deviation (MAD)0.8599422872
Skewness0.006395054405
Sum-1.758540748e-06
Variance0.9999999967
2020-08-25T00:23:47.823269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.60546326610.1%
 
0.384090244810.1%
 
-0.343012094510.1%
 
-0.641253173410.1%
 
-0.264483690310.1%
 
-1.23649513710.1%
 
-1.48987805810.1%
 
0.680302798710.1%
 
-1.20952105510.1%
 
0.901002287910.1%
 
0.425109684510.1%
 
0.821999907510.1%
 
-1.17309093510.1%
 
0.208647429910.1%
 
-0.848259031810.1%
 
0.121899619710.1%
 
-1.0792858610.1%
 
0.387018591210.1%
 
1.53786671210.1%
 
0.223216399610.1%
 
1.53313744110.1%
 
0.650985240910.1%
 
0.658797025710.1%
 
-0.576764583610.1%
 
0.801372826110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.72962415210.1%
 
-1.72203242810.1%
 
-1.71727359310.1%
 
-1.71545934710.1%
 
-1.7137582310.1%
 
-1.7107158910.1%
 
-1.70932805510.1%
 
-1.70905435110.1%
 
-1.70733237310.1%
 
-1.70563352110.1%
 
ValueCountFrequency (%) 
1.74932038810.1%
 
1.74381184610.1%
 
1.74295830710.1%
 
1.7390832910.1%
 
1.73906898510.1%
 
1.73792529110.1%
 
1.73780822810.1%
 
1.73771393310.1%
 
1.73235297210.1%
 
1.7263152610.1%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.306524250656366e-10
Minimum-1.7511482238769531
Maximum1.6476068496704102
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:47.936672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.751148224
5-th percentile-1.58363409
Q1-0.8832782358
median0.03146253061
Q30.8649162054
95-th percentile1.500237477
Maximum1.64760685
Range3.398755074
Interquartile range (IQR)1.748194441

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1203872969
Kurtosis-1.23482073
Mean-8.306524251e-10
Median Absolute Deviation (MAD)0.8735519648
Skewness-0.0609669312
Sum-8.306524251e-07
Variance1.000000002
2020-08-25T00:23:48.041814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.64875805410.1%
 
-0.495428115110.1%
 
0.361651778210.1%
 
-1.09026110210.1%
 
-1.30151772510.1%
 
0.327470034410.1%
 
0.0859258472910.1%
 
-1.54033851610.1%
 
0.566843509710.1%
 
-0.79253017910.1%
 
0.777975261210.1%
 
1.52079117310.1%
 
-0.883441269410.1%
 
0.208164140610.1%
 
-0.654920399210.1%
 
-1.28248965710.1%
 
0.828711211710.1%
 
-1.38404631610.1%
 
-1.47778737510.1%
 
-0.401192635310.1%
 
-0.93420493610.1%
 
-0.844358801810.1%
 
-0.110519692310.1%
 
-0.572872936710.1%
 
-1.32543051210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75114822410.1%
 
-1.7487531910.1%
 
-1.74771010910.1%
 
-1.74612259910.1%
 
-1.74481785310.1%
 
-1.74126207810.1%
 
-1.73959362510.1%
 
-1.73595976810.1%
 
-1.73352849510.1%
 
-1.73060655610.1%
 
ValueCountFrequency (%) 
1.6476068510.1%
 
1.63875269910.1%
 
1.63831043210.1%
 
1.63785290710.1%
 
1.63433575610.1%
 
1.63029253510.1%
 
1.62785577810.1%
 
1.62727642110.1%
 
1.6264669910.1%
 
1.62331998310.1%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5991972750484252e-09
Minimum-1.7368309497833252
Maximum1.7221657037734983
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:48.158716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.73683095
5-th percentile-1.567275852
Q1-0.8530091792
median-0.004024858237
Q30.8800756633
95-th percentile1.52456156
Maximum1.722165704
Range3.458996654
Interquartile range (IQR)1.733084843

Descriptive statistics

Standard deviation0.9999999994
Coefficient of variation (CV)-625313721.5
Kurtosis-1.213218283
Mean-1.599197275e-09
Median Absolute Deviation (MAD)0.8708190372
Skewness-0.006957468461
Sum-1.599197275e-06
Variance0.9999999988
2020-08-25T00:23:48.263083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.603509366510.1%
 
1.51302456910.1%
 
1.61076104610.1%
 
0.690145134910.1%
 
0.690419137510.1%
 
-0.822947919410.1%
 
0.873723745310.1%
 
-0.322714090310.1%
 
-1.24352955810.1%
 
-0.620200693610.1%
 
1.48570835610.1%
 
1.44273531410.1%
 
0.368495166310.1%
 
0.473963260710.1%
 
-1.41665470610.1%
 
-0.361653417310.1%
 
0.967796146910.1%
 
0.388996481910.1%
 
-0.310870319610.1%
 
0.368487983910.1%
 
-0.723370194410.1%
 
0.781891465210.1%
 
0.227378919710.1%
 
-1.34502720810.1%
 
0.017855456110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7368309510.1%
 
-1.73664367210.1%
 
-1.73472225710.1%
 
-1.732176910.1%
 
-1.73215913810.1%
 
-1.72894883210.1%
 
-1.72142374510.1%
 
-1.7207536710.1%
 
-1.71656584710.1%
 
-1.71625697610.1%
 
ValueCountFrequency (%) 
1.72216570410.1%
 
1.71654915810.1%
 
1.70632374310.1%
 
1.70505404510.1%
 
1.70011305810.1%
 
1.69873285310.1%
 
1.69366538510.1%
 
1.69186186810.1%
 
1.69092905510.1%
 
1.6838169110.1%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.10832716524601e-10
Minimum-1.731738567352295
Maximum1.6535446643829346
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:48.383337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.731738567
5-th percentile-1.574241781
Q1-0.8937137276
median0.02538863942
Q30.8974024951
95-th percentile1.514068335
Maximum1.653544664
Range3.385283232
Interquartile range (IQR)1.791116223

Descriptive statistics

Standard deviation0.9999999998
Coefficient of variation (CV)1233300013
Kurtosis-1.23293555
Mean8.108327165e-10
Median Absolute Deviation (MAD)0.896782726
Skewness-0.04316194024
Sum8.108327165e-07
Variance0.9999999997
2020-08-25T00:23:48.487489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.05077886610.1%
 
-0.635427713410.1%
 
0.672380268610.1%
 
-1.64154374610.1%
 
-1.48966205110.1%
 
1.46231508310.1%
 
-0.0469179563210.1%
 
-1.21033358610.1%
 
1.19666290310.1%
 
-1.03650605710.1%
 
0.520766317810.1%
 
-0.24137881410.1%
 
1.41540324710.1%
 
1.57288205610.1%
 
0.194449633410.1%
 
-0.289393246210.1%
 
-0.651021838210.1%
 
0.514328241310.1%
 
-0.429129809110.1%
 
-0.215982437110.1%
 
-0.272784620510.1%
 
0.139810234310.1%
 
0.323565304310.1%
 
0.895645141610.1%
 
1.07159864910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.73173856710.1%
 
-1.73127865810.1%
 
-1.7292475710.1%
 
-1.72835874610.1%
 
-1.72815048710.1%
 
-1.72524249610.1%
 
-1.72181940110.1%
 
-1.71750915110.1%
 
-1.71527016210.1%
 
-1.7124526510.1%
 
ValueCountFrequency (%) 
1.65354466410.1%
 
1.64935398110.1%
 
1.64614999310.1%
 
1.63951969110.1%
 
1.63631200810.1%
 
1.63618278510.1%
 
1.63573467710.1%
 
1.6349970110.1%
 
1.63318669810.1%
 
1.62138140210.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.695634840487741e-10
Minimum-2.6269795894622803
Maximum2.8341217041015625
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:48.606542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.626979589
5-th percentile-1.676577115
Q1-0.7195919007
median0.02911405172
Q30.7188377231
95-th percentile1.633621031
Maximum2.834121704
Range5.461101294
Interquartile range (IQR)1.438429624

Descriptive statistics

Standard deviation0.9999999985
Coefficient of variation (CV)-1299437953
Kurtosis-0.509296179
Mean-7.69563484e-10
Median Absolute Deviation (MAD)0.7186793387
Skewness-0.05403030424
Sum-7.69563484e-07
Variance0.999999997
2020-08-25T00:23:48.713928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.869138538810.1%
 
0.00593061558910.1%
 
-1.60682165610.1%
 
-0.247879937310.1%
 
1.66930365610.1%
 
0.343942731610.1%
 
-0.961601257310.1%
 
-1.25132298510.1%
 
0.707690298610.1%
 
-0.557297885410.1%
 
1.2786554110.1%
 
-0.518231272710.1%
 
1.37630236110.1%
 
2.03384566310.1%
 
0.758460879310.1%
 
-0.71939617410.1%
 
-1.27257287510.1%
 
1.06121015510.1%
 
0.435868889110.1%
 
-0.346023559610.1%
 
-0.35188287510.1%
 
-1.11065292410.1%
 
-0.0686826482410.1%
 
-0.0314708054110.1%
 
-0.214406162510.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.62697958910.1%
 
-2.57510638210.1%
 
-2.52819228210.1%
 
-2.40822577510.1%
 
-2.33275318110.1%
 
-2.30712056210.1%
 
-2.28593730910.1%
 
-2.28257632310.1%
 
-2.27394700110.1%
 
-2.24796652810.1%
 
ValueCountFrequency (%) 
2.83412170410.1%
 
2.48503613510.1%
 
2.33674669310.1%
 
2.3352153310.1%
 
2.21990799910.1%
 
2.21318101910.1%
 
2.15934920310.1%
 
2.12579059610.1%
 
2.1142928610.1%
 
2.07131123510.1%
 

Interactions

2020-08-25T00:23:25.264200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:25.424483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:25.589582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:25.752828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:25.913321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.074466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.238824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.400203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.562994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.730555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:26.897563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:27.053663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:27.222789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:27.392508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:27.560822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:27.738681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.063284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.231765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.399010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.565767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.737020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:28.902619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.066230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.231295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.396739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.558584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.730347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:29.892776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.057709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.220834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.382943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.544679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.717562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:30.877498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.038532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.204256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.367656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.529072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.697745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:31.863284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:32.024715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:32.185685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:32.350924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:32.515683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:32.677542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.014748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.180693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.346841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.511250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.674679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:33.840400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.002639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.164364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.327397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.488461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.643328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.820752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:34.985259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.145807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.309396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.476390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.636521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.794914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:35.964958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.134085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.296636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.457937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.624812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.789555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:36.951539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:37.113844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:37.276910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:37.440012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:37.601755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:37.937921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.098549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.259547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.422547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.586948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.753557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:38.918467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.079934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.240299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.403064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.565950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.725565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:39.887793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.052295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.208891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.370366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.537656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.697504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:40.858688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.023677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.184484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.345296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.508796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.667661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.828043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:41.987822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:42.146135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:42.311611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:42.476500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:42.823012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:42.984913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.146649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.308521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.474499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.638053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.798333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:43.955842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.113934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.272380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.427618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.585092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.741395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:44.899215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:45.057855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:45.213789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:45.369452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:45.538489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:23:48.841630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:23:49.063620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:23:49.284152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:23:49.504642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:23:45.800013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:46.072567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.5467211.447739-0.678404-1.0744680.593252-1.347680-0.9743460.277059-0.225340-0.0556110.237039
10.0673921.430332-0.6298951.4716291.6500491.0884640.872563-1.6065381.478605-1.1286472.051099
2-0.179446-1.4667501.0856001.132297-0.5598580.6408361.589126-0.729912-0.1729670.4898050.076346
3-1.389567-0.861964-0.2069091.1474870.324989-1.5925900.728129-1.332016-0.443414-0.194433-0.348233
40.191729-0.410029-1.613817-0.3733160.267076-0.930757-0.0534960.5236180.508390-0.9150130.616146
51.6919850.495551-1.568475-0.8743110.347002-0.176720-0.421768-1.265787-1.114318-1.1046120.956251
61.5579530.5323311.413901-0.975579-1.688089-0.5463821.4413571.6378530.9699810.605462-0.213124
71.1416380.0104291.5559100.4511391.5793160.366656-0.3998781.448090-0.3631351.3024511.997359
8-1.760759-0.562962-0.327352-0.302249-1.5856391.5074520.288676-0.009782-1.462590-1.285269-2.168530
91.2141640.284620-1.7084740.8576310.017894-0.230135-0.875768-0.9880130.182935-1.6855491.949501

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
990-0.6724110.3726771.476570-0.7312561.2302280.0824200.929319-1.719407-1.193580-1.2398240.175354
991-0.4071931.4691381.2466721.1381140.478525-1.669238-1.3055680.922549-1.1798150.1942702.055447
9920.586457-0.976276-0.7826001.4677060.980697-0.888069-1.309309-0.102641-0.623979-0.9911110.630112
9931.291706-0.843951-0.5403410.989470-0.5156570.537105-1.5004951.418776-1.619297-0.0503350.406833
994-0.305086-0.6644920.0759130.022840-0.257327-0.777069-0.1772721.390136-0.7233700.318787-0.665702
995-0.5527990.8114651.1826091.3146261.461684-0.005911-0.2807290.928222-0.9774080.1620241.540605
9960.9155491.268860-1.571592-0.078985-0.581010-0.300245-0.906219-0.866722-1.0088710.9646991.253992
997-1.1702490.1831800.9712321.5555160.208108-0.198330-0.0303911.1994980.9288251.5673020.440867
998-0.6445990.580665-0.624013-1.226447-1.5005811.386233-1.3898891.225052-0.1100290.269316-1.061910
9991.511915-0.763444-0.080229-0.556763-0.327957-0.518716-1.164918-1.5240711.1155731.204698-0.491797